ArticleJournal of the American Geriatrics Society2020
Serious Falls in Middle-Aged Veterans: Development and Validation of a Predictive Risk Model.
Article in Journal of the American Geriatrics Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 17 citations in OpenAlex.
- Developing an HIV-specific falls risk prediction model with a novel clinical index: a systematic review and meta-analysis method.BMC infectious diseases · 2024Pooled it
- A systematic review of chiropractic care for fall prevention: rationale, state of the evidence, and recommendations for future research.BMC musculoskeletal disorders · 2022Pooled it
- Alcohol Consumption During Muscle Disuse Causes Differential Signaling Responses in a Muscle-Specific Manner in Mice.International journal of molecular sciences · 2026Article
- Association Between Polypharmacy and Physical Function in Middle-aged Adults: Findings from the CARDIA Function Study.Journal of general internal medicine · 2026Article
- A scoping review of machine learning models to predict risk of falls in elders, without using sensor data.Diagnostic and prognostic research · 2025Review
- Article
- Gerontologic Biostatistics and Data Science: Aging Research in the Era of Big Data.The journals of gerontology. Series A, Biological sciences and medical sciences · 2024Review
- Risk factors for suicide among veterans living with and without HIV: a nested case-control study.AIDS and behavior · 2024Article
- Assessing the contributions of modifiable risk factors to serious falls and fragility fractures among older persons living with HIV.Journal of the American Geriatrics Society · 2023Article
- Validation of the Persian version of the 9-item Berg Balance Scale Among Older Iranians.Oman medical journal · 2023Article
- Development and external validation of a risk prediction model for falls in patients with an indication for antihypertensive treatment: retrospective cohort study.BMJ (Clinical research ed.) · 2022Article
- Potentially inappropriate medication use by level of polypharmacy among US Veterans 49-64 and 65-70 years old.Pharmacoepidemiology and drug safety · 2022Article
- Development and Internal Validation of a Risk Prediction Model for Falls Among Older People Using Primary Care Electronic Health Records.The journals of gerontology. Series A, Biological sciences and medical sciences · 2022Article
- Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature.Applied clinical informatics · 2022Review
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 1 country.
Funding
Abstract
BACKGROUND/
objectivesDue to high rates of multimorbidity, polypharmacy, and hazardous alcohol and opioid use, middle-aged Veterans are at risk for serious falls (those prompting a visit with a healthcare provider), posing significant risk to their forthcoming geriatric health and quality of life. We developed and validated a predictive model of the 6-month risk of serious falls among middle-aged Veterans.
designCohort study.
settingVeterans Health Administration (VA).
participantsVeterans, aged 45 to 65 years, who presented for care within the VA between 2012 and 2015 (N = 275,940). EXPOSURES: The exposures of primary interest were substance use (including alcohol and prescription opioid use), multimorbidity, and polypharmacy. Hazardous alcohol use was defined as an Alcohol Use Disorders Identification Test - Consumption (AUDIT-C) score of 3 or greater for women and 4 or greater for men. We used International Classification of Diseases, Ninth Revision (ICD-9), codes to identify alcohol and illicit substance use disorders and identified prescription opioid use from pharmacy fill-refill data. We included counts of chronic medications and of physical and mental health comorbidities. MEASUREMENTS: We identified serious falls using external cause of injury codes and a machine-learning algorithm that identified serious falls in radiology reports. We used multivariable logistic regression with general estimating equations to calculate risk. We used an integrated predictiveness curve to identify intervention thresholds.
resultsMost of our sample (54%) was aged 60 years or younger. Duration of follow-up was up to 4 years. Veterans who fell were more likely to be female (11% vs 7%) and White (72% vs 68%). They experienced 43,641 serious falls during follow-up. We identified 16 key predictors of serious falls and five interaction terms. Model performance was enhanced by addition of opioid use, as evidenced by overall category-free net reclassification improvement of 0.32 (P < .001). Discrimination (C-statistic = 0.76) and calibration were excellent for both development and validation data sets.
conclusionWe developed and internally validated a model to predict 6-month risk of serious falls among middle-aged Veterans with excellent discrimination and calibration.
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